The use of computer models and machine-learning algorithms could reduce healthcare costs and improve patient outcomes, according to a study recently published in the journal Artificial Intelligence in Medicine.
For the study, researchers at Indiana University randomly selected 500 patients from the Centerstone Research Institute database. The database houses clinical data, demographics and other information on 6,700 patients, 60% to 70% of whom had both clinical depression and a concurrent physical disorder such as diabetes or hypertension.
Researchers then used the selected patients' data to compare actual doctor performance and patient outcomes against simulated outcomes generated by an artificial intelligence framework. The framework combines two mathematical modeling formulas, known as Markov Decision Processes and Dynamic Decision Networks.
By running the patient data through the machine-learning algorithm, researchers found that the artificial intelligence framework could have generated a 30% to 35% increase in positive patient outcomes.